Are CO₂ Emissions a Health Hazard or Just a Side Effect of Progress?#
With the data prepared, we now present three visualizations supporting each perspective. Each visualization is accompanied by a brief insight and an explanation linking it to the respective argument.
Perspective 1:#
Do Higher CO₂ Emissions Shorten Healthy Lifespans? This perspective expects to see negative correlations or warning signs that pollution from CO₂ emissions adversely affects health. We look for evidence that countries with higher emissions have lower healthy life expectancy, or that rapid emission growth is associated with stagnating health outcomes.
Visualization 1.#
CO₂ per Capita vs Healthy Life Expectancy (2019) – Is higher carbon output linked to lower healthy life expectancy?
Show code cell source
# ── Complete Interactieve Grouped Barplot voor 2019 ──
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
# Renderer instellen voor inline weergave
pio.renderers.default = "notebook_connected"
# 1) Data inladen
co2 = pd.read_csv('owid-co2-data.csv', usecols=['country','year','co2_per_capita'])
hale = pd.read_csv('C64284D_ALL_LATEST.csv')
# 2) Healthy life expectancy filter & renamen
hale = (
hale.loc[
(hale['IND_NAME']=='Healthy life expectancy (at birth)') &
(hale['DIM_SEX']=='TOTAL'),
['GEO_NAME_SHORT','DIM_TIME','AMOUNT_N']
]
.rename(columns={
'GEO_NAME_SHORT':'country',
'DIM_TIME':'year',
'AMOUNT_N':'healthy_life_expectancy'
})
)
# 3) Jaar-kolommen als int
co2['year'] = co2['year'].astype(int)
hale['year'] = hale['year'].astype(int)
# 4) Harmoniseer landnaam en merge voor 2019
co2['country'] = co2['country'].replace({'United States':'United States of America'})
merged = pd.merge(co2, hale, on=['country','year'], how='inner')
df2019 = merged[merged['year']==2019].dropna(subset=['co2_per_capita','healthy_life_expectancy'])
# 5) Specifieke landen in juiste volgorde
countries = [
'United States of America',
'France',
'India'
]
df3 = df2019.set_index('country').loc[countries]
# 6) X-positie en barbreedte
x = np.arange(len(countries))
width = 0.35
# 7) Bouw de interactieve figuur
fig = go.Figure()
# CO₂ per Capita (linker y-as)
fig.add_trace(go.Bar(
x=x - width/2,
y=df3['co2_per_capita'],
name='CO₂ per Capita (tons)',
marker_color='orange',
marker_line_width=1,
marker_line_color='black',
hovertemplate='CO₂: %{y:.2f} ton<extra></extra>',
yaxis='y1'
))
# Healthy Life Expectancy (rechter y-as)
fig.add_trace(go.Bar(
x=x + width/2,
y=df3['healthy_life_expectancy'],
name='Healthy Life Expectancy (years)',
marker_color='blue',
marker_line_width=1,
marker_line_color='black',
hovertemplate='Gezonde levensverwachting: %{y:.1f} jaar<extra></extra>',
yaxis='y2'
))
# 8) Layout met dubbele y-as en styling
fig.update_layout(
title="Emissions vs Healthy Life Expectancy: U.S. vs France vs India (2019)",
xaxis=dict(
tickmode='array',
tickvals=x,
ticktext=countries,
tickangle=15,
title=dict(text='Country')
),
yaxis=dict(
title=dict(text='CO₂ per Capita (tons)', font=dict(color='orange')),
tickfont=dict(color='orange')
),
yaxis2=dict(
title=dict(text='Healthy Life Expectancy (years)', font=dict(color='blue')),
tickfont=dict(color='blue'),
overlaying='y',
side='right'
),
legend=dict(
orientation='h',
yanchor='bottom',
y=1.02,
x=0.5,
xanchor='center'
),
bargap=0.2,
margin=dict(t=80, b=50, l=60, r=60)
)
# 9) Toon de interactieve grafiek
fig.show()
# (Optioneel) Exporteer als standalone HTML
# fig.write_html("emissions_health_2019.html", include_plotlyjs='cdn')
Caption: Each point is a country in 2019. We might expect a downward trend if emissions were broadly harming health, but the pattern is not clear-cut. Many high-emission countries (far right) actually enjoy high healthy life expectancy, while low-emission countries (far left) often have low healthy life expectancy. The fitted trendline (from an OLS regression) is nearly flat, indicating no strong linear relationship globally.
Insight: At a global level, there is no obvious negative correlation between CO₂ emissions per person and healthy life expectancy. For example, the United States and Gulf countries have some of the highest per-capita CO₂ emissions yet still report healthy life expectancies around 65–70+ years. In contrast, countries with minimal emissions (mostly low-income nations) cluster in the lower-left, with healthy life expectancy often below 60 years. This suggests that factors other than emissions (like economic development) are dominating the health outcomes. However, supporters of Perspective 1 point out that this global view could mask specific health costs of emissions for instance, chronic air pollution in rapidly industrializing countries might be limiting further health gains. The lack of a clear inverse trend here hints that CO₂’s impact on health is indirect and tangled with development rather than a simple one-to-one effect.
Visualization 2. Emissions and Health in Selected Countries (2019)#
– Case comparison of a high emitter vs. a low emitter vs. a moderate emitter.
Show code cell source
import pandas as pd
import plotly.graph_objects as go
import numpy as np
# 1) Data inladen en voorbereiden
co2 = pd.read_csv('owid-co2-data.csv', usecols=['country','year','co2_per_capita'])
hale = pd.read_csv('C64284D_ALL_LATEST.csv')
# Harmoniseer landnaam
co2['country'] = co2['country'].replace({'United States':'United States of America'})
# Filter Healthy life expectancy
hale = (
hale.loc[
(hale['IND_NAME']=='Healthy life expectancy (at birth)') &
(hale['DIM_SEX']=='TOTAL'),
['GEO_NAME_SHORT','DIM_TIME','AMOUNT_N']
]
.rename(columns={
'GEO_NAME_SHORT':'country',
'DIM_TIME':'year',
'AMOUNT_N':'healthy_life_expectancy'
})
)
# Jaar als int, merge en filter voor 2019 + geselecteerde landen
co2['year'] = co2['year'].astype(int)
hale['year'] = hale['year'].astype(int)
merged = pd.merge(co2, hale, on=['country','year'], how='inner')
countries = [
'United States of America',
'France',
'India'
]
df2019 = (
merged
.query("year == 2019 and country in @countries")
.set_index('country')
.loc[countries]
.reset_index()
)
# 2) X-waarden en barbreedte
x = np.arange(len(countries))
width = 0.4
# 3) Bouw de interactieve figuur
fig = go.Figure()
# CO₂ per Capita (linker y-as)
fig.add_trace(go.Bar(
x=x - width/2,
y=df2019['co2_per_capita'],
name='CO₂ per Capita (tons)',
marker_color='orange',
yaxis='y1',
hovertemplate='CO₂: %{y} ton<extra></extra>'
))
# Healthy Life Expectancy (rechter y-as)
fig.add_trace(go.Bar(
x=x + width/2,
y=df2019['healthy_life_expectancy'],
name='Healthy Life Expectancy (years)',
marker_color='blue',
yaxis='y2',
hovertemplate='Gezonde levensverwachting: %{y} jaar<extra></extra>'
))
# 4) Layout met dubbele y-as en styling gelijk aan matplotlib
fig.update_layout(
title="Emissions vs Healthy Life Expectancy: U.S. vs France vs India (2019)",
xaxis=dict(
tickmode='array',
tickvals=x,
ticktext=countries,
tickangle=15,
title='Country'
),
yaxis=dict(
title='CO₂ per Capita (tons)',
titlefont=dict(color='orange'),
tickfont=dict(color='orange')
),
yaxis2=dict(
title='Healthy Life Expectancy (years)',
titlefont=dict(color='blue'),
tickfont=dict(color='blue'),
overlaying='y',
side='right'
),
legend=dict(
orientation='h',
yanchor='bottom',
y=1.02,
x=0.5,
xanchor='center'
),
bargap=0.2,
margin=dict(t=80, b=50, l=60, r=60)
)
# 5) Toon interactieve grafiek
fig.show()
# Optioneel: exporteer naar standalone HTML
# fig.write_html("emissions_health_2019.html", include_plotlyjs='cdn')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 73
63 fig.add_trace(go.Bar(
64 x=x + width/2,
65 y=df2019['healthy_life_expectancy'],
(...) 69 hovertemplate='Gezonde levensverwachting: %{y} jaar<extra></extra>'
70 ))
72 # 4) Layout met dubbele y-as en styling gelijk aan matplotlib
---> 73 fig.update_layout(
74 title="Emissions vs Healthy Life Expectancy: U.S. vs France vs India (2019)",
75 xaxis=dict(
76 tickmode='array',
77 tickvals=x,
78 ticktext=countries,
79 tickangle=15,
80 title='Country'
81 ),
82 yaxis=dict(
83 title='CO₂ per Capita (tons)',
84 titlefont=dict(color='orange'),
85 tickfont=dict(color='orange')
86 ),
87 yaxis2=dict(
88 title='Healthy Life Expectancy (years)',
89 titlefont=dict(color='blue'),
90 tickfont=dict(color='blue'),
91 overlaying='y',
92 side='right'
93 ),
94 legend=dict(
95 orientation='h',
96 yanchor='bottom',
97 y=1.02,
98 x=0.5,
99 xanchor='center'
100 ),
101 bargap=0.2,
102 margin=dict(t=80, b=50, l=60, r=60)
103 )
105 # 5) Toon interactieve grafiek
106 fig.show()
File ~\AppData\Local\Programs\Python\Python313\Lib\site-packages\plotly\graph_objs\_figure.py:219, in Figure.update_layout(self, dict1, overwrite, **kwargs)
193 def update_layout(self, dict1=None, overwrite=False, **kwargs) -> "Figure":
194 """
195
196 Update the properties of the figure's layout with a dict and/or with
(...) 217
218 """
--> 219 return super().update_layout(dict1, overwrite, **kwargs)
File ~\AppData\Local\Programs\Python\Python313\Lib\site-packages\plotly\basedatatypes.py:1413, in BaseFigure.update_layout(self, dict1, overwrite, **kwargs)
1389 def update_layout(self, dict1=None, overwrite=False, **kwargs):
1390 """
1391 Update the properties of the figure's layout with a dict and/or with
1392 keyword arguments.
(...) 1411 The Figure object that the update_layout method was called on
1412 """
-> 1413 self.layout.update(dict1, overwrite=overwrite, **kwargs)
1414 return self
File ~\AppData\Local\Programs\Python\Python313\Lib\site-packages\plotly\basedatatypes.py:5215, in BasePlotlyType.update(self, dict1, overwrite, **kwargs)
5213 with self.figure.batch_update():
5214 BaseFigure._perform_update(self, dict1, overwrite=overwrite)
-> 5215 BaseFigure._perform_update(self, kwargs, overwrite=overwrite)
5216 else:
5217 BaseFigure._perform_update(self, dict1, overwrite=overwrite)
File ~\AppData\Local\Programs\Python\Python313\Lib\site-packages\plotly\basedatatypes.py:3989, in BaseFigure._perform_update(plotly_obj, update_obj, overwrite)
3983 validator = plotly_obj._get_prop_validator(key)
3985 if isinstance(validator, CompoundValidator) and isinstance(val, dict):
3986
3987 # Update compound objects recursively
3988 # plotly_obj[key].update(val)
-> 3989 BaseFigure._perform_update(plotly_obj[key], val)
3990 elif isinstance(validator, CompoundArrayValidator):
3991 if plotly_obj[key]:
3992 # plotly_obj has an existing non-empty array for key
3993 # In this case we merge val into the existing elements
File ~\AppData\Local\Programs\Python\Python313\Lib\site-packages\plotly\basedatatypes.py:3966, in BaseFigure._perform_update(plotly_obj, update_obj, overwrite)
3964 err = _check_path_in_prop_tree(plotly_obj, key, error_cast=ValueError)
3965 if err is not None:
-> 3966 raise err
3968 # Convert update_obj to dict
3969 # --------------------------
3970 if isinstance(update_obj, BasePlotlyType):
ValueError: Invalid property specified for object of type plotly.graph_objs.layout.YAxis: 'titlefont'
Did you mean "tickfont"?
Valid properties:
anchor
If set to an opposite-letter axis id (e.g. `x2`, `y`),
this axis is bound to the corresponding opposite-letter
axis. If set to "free", this axis' position is
determined by `position`.
automargin
Determines whether long tick labels automatically grow
the figure margins.
autorange
Determines whether or not the range of this axis is
computed in relation to the input data. See `rangemode`
for more info. If `range` is provided and it has a
value for both the lower and upper bound, `autorange`
is set to False. Using "min" applies autorange only to
set the minimum. Using "max" applies autorange only to
set the maximum. Using *min reversed* applies autorange
only to set the minimum on a reversed axis. Using *max
reversed* applies autorange only to set the maximum on
a reversed axis. Using "reversed" applies autorange on
both ends and reverses the axis direction.
autorangeoptions
:class:`plotly.graph_objects.layout.yaxis.Autorangeopti
ons` instance or dict with compatible properties
autoshift
Automatically reposition the axis to avoid overlap with
other axes with the same `overlaying` value. This
repositioning will account for any `shift` amount
applied to other axes on the same side with `autoshift`
is set to true. Only has an effect if `anchor` is set
to "free".
autotickangles
When `tickangle` is set to "auto", it will be set to
the first angle in this array that is large enough to
prevent label overlap.
autotypenumbers
Using "strict" a numeric string in trace data is not
converted to a number. Using *convert types* a numeric
string in trace data may be treated as a number during
automatic axis `type` detection. Defaults to
layout.autotypenumbers.
calendar
Sets the calendar system to use for `range` and `tick0`
if this is a date axis. This does not set the calendar
for interpreting data on this axis, that's specified in
the trace or via the global `layout.calendar`
categoryarray
Sets the order in which categories on this axis appear.
Only has an effect if `categoryorder` is set to
"array". Used with `categoryorder`.
categoryarraysrc
Sets the source reference on Chart Studio Cloud for
`categoryarray`.
categoryorder
Specifies the ordering logic for the case of
categorical variables. By default, plotly uses "trace",
which specifies the order that is present in the data
supplied. Set `categoryorder` to *category ascending*
or *category descending* if order should be determined
by the alphanumerical order of the category names. Set
`categoryorder` to "array" to derive the ordering from
the attribute `categoryarray`. If a category is not
found in the `categoryarray` array, the sorting
behavior for that attribute will be identical to the
"trace" mode. The unspecified categories will follow
the categories in `categoryarray`. Set `categoryorder`
to *total ascending* or *total descending* if order
should be determined by the numerical order of the
values. Similarly, the order can be determined by the
min, max, sum, mean, geometric mean or median of all
the values.
color
Sets default for all colors associated with this axis
all at once: line, font, tick, and grid colors. Grid
color is lightened by blending this with the plot
background Individual pieces can override this.
constrain
If this axis needs to be compressed (either due to its
own `scaleanchor` and `scaleratio` or those of the
other axis), determines how that happens: by increasing
the "range", or by decreasing the "domain". Default is
"domain" for axes containing image traces, "range"
otherwise.
constraintoward
If this axis needs to be compressed (either due to its
own `scaleanchor` and `scaleratio` or those of the
other axis), determines which direction we push the
originally specified plot area. Options are "left",
"center" (default), and "right" for x axes, and "top",
"middle" (default), and "bottom" for y axes.
dividercolor
Sets the color of the dividers Only has an effect on
"multicategory" axes.
dividerwidth
Sets the width (in px) of the dividers Only has an
effect on "multicategory" axes.
domain
Sets the domain of this axis (in plot fraction).
dtick
Sets the step in-between ticks on this axis. Use with
`tick0`. Must be a positive number, or special strings
available to "log" and "date" axes. If the axis `type`
is "log", then ticks are set every 10^(n*dtick) where n
is the tick number. For example, to set a tick mark at
1, 10, 100, 1000, ... set dtick to 1. To set tick marks
at 1, 100, 10000, ... set dtick to 2. To set tick marks
at 1, 5, 25, 125, 625, 3125, ... set dtick to
log_10(5), or 0.69897000433. "log" has several special
values; "L<f>", where `f` is a positive number, gives
ticks linearly spaced in value (but not position). For
example `tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus
small digits between, use "D1" (all digits) or "D2"
(only 2 and 5). `tick0` is ignored for "D1" and "D2".
If the axis `type` is "date", then you must convert the
time to milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to 86400000.0.
"date" also has special values "M<n>" gives ticks
spaced by a number of months. `n` must be a positive
integer. To set ticks on the 15th of every third month,
set `tick0` to "2000-01-15" and `dtick` to "M3". To set
ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick exponents.
For example, consider the number 1,000,000,000. If
"none", it appears as 1,000,000,000. If "e", 1e+9. If
"E", 1E+9. If "power", 1x10^9 (with 9 in a super
script). If "SI", 1G. If "B", 1B.
fixedrange
Determines whether or not this axis is zoom-able. If
true, then zoom is disabled.
gridcolor
Sets the color of the grid lines.
griddash
Sets the dash style of lines. Set to a dash type string
("solid", "dot", "dash", "longdash", "dashdot", or
"longdashdot") or a dash length list in px (eg
"5px,10px,2px,2px").
gridwidth
Sets the width (in px) of the grid lines.
hoverformat
Sets the hover text formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display "09~15~23.46"
insiderange
Could be used to set the desired inside range of this
axis (excluding the labels) when `ticklabelposition` of
the anchored axis has "inside". Not implemented for
axes with `type` "log". This would be ignored when
`range` is provided.
labelalias
Replacement text for specific tick or hover labels. For
example using {US: 'USA', CA: 'Canada'} changes US to
USA and CA to Canada. The labels we would have shown
must match the keys exactly, after adding any
tickprefix or ticksuffix. For negative numbers the
minus sign symbol used (U+2212) is wider than the
regular ascii dash. That means you need to use −1
instead of -1. labelalias can be used with any axis
type, and both keys (if needed) and values (if desired)
can include html-like tags or MathJax.
layer
Sets the layer on which this axis is displayed. If
*above traces*, this axis is displayed above all the
subplot's traces If *below traces*, this axis is
displayed below all the subplot's traces, but above the
grid lines. Useful when used together with scatter-like
traces with `cliponaxis` set to False to show markers
and/or text nodes above this axis.
linecolor
Sets the axis line color.
linewidth
Sets the width (in px) of the axis line.
matches
If set to another axis id (e.g. `x2`, `y`), the range
of this axis will match the range of the corresponding
axis in data-coordinates space. Moreover, matching axes
share auto-range values, category lists and histogram
auto-bins. Note that setting axes simultaneously in
both a `scaleanchor` and a `matches` constraint is
currently forbidden. Moreover, note that matching axes
must have the same `type`.
maxallowed
Determines the maximum range of this axis.
minallowed
Determines the minimum range of this axis.
minexponent
Hide SI prefix for 10^n if |n| is below this number.
This only has an effect when `tickformat` is "SI" or
"B".
minor
:class:`plotly.graph_objects.layout.yaxis.Minor`
instance or dict with compatible properties
mirror
Determines if the axis lines or/and ticks are mirrored
to the opposite side of the plotting area. If True, the
axis lines are mirrored. If "ticks", the axis lines and
ticks are mirrored. If False, mirroring is disable. If
"all", axis lines are mirrored on all shared-axes
subplots. If "allticks", axis lines and ticks are
mirrored on all shared-axes subplots.
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks will be
chosen automatically to be less than or equal to
`nticks`. Has an effect only if `tickmode` is set to
"auto".
overlaying
If set a same-letter axis id, this axis is overlaid on
top of the corresponding same-letter axis, with traces
and axes visible for both axes. If False, this axis
does not overlay any same-letter axes. In this case,
for axes with overlapping domains only the highest-
numbered axis will be visible.
position
Sets the position of this axis in the plotting space
(in normalized coordinates). Only has an effect if
`anchor` is set to "free".
range
Sets the range of this axis. If the axis `type` is
"log", then you must take the log of your desired range
(e.g. to set the range from 1 to 100, set the range
from 0 to 2). If the axis `type` is "date", it should
be date strings, like date data, though Date objects
and unix milliseconds will be accepted and converted to
strings. If the axis `type` is "category", it should be
numbers, using the scale where each category is
assigned a serial number from zero in the order it
appears. Leaving either or both elements `null` impacts
the default `autorange`.
rangebreaks
A tuple of
:class:`plotly.graph_objects.layout.yaxis.Rangebreak`
instances or dicts with compatible properties
rangebreakdefaults
When used in a template (as
layout.template.layout.yaxis.rangebreakdefaults), sets
the default property values to use for elements of
layout.yaxis.rangebreaks
rangemode
If "normal", the range is computed in relation to the
extrema of the input data. If "tozero", the range
extends to 0, regardless of the input data If
"nonnegative", the range is non-negative, regardless of
the input data. Applies only to linear axes.
scaleanchor
If set to another axis id (e.g. `x2`, `y`), the range
of this axis changes together with the range of the
corresponding axis such that the scale of pixels per
unit is in a constant ratio. Both axes are still
zoomable, but when you zoom one, the other will zoom
the same amount, keeping a fixed midpoint. `constrain`
and `constraintoward` determine how we enforce the
constraint. You can chain these, ie `yaxis:
{scaleanchor: *x*}, xaxis2: {scaleanchor: *y*}` but you
can only link axes of the same `type`. The linked axis
can have the opposite letter (to constrain the aspect
ratio) or the same letter (to match scales across
subplots). Loops (`yaxis: {scaleanchor: *x*}, xaxis:
{scaleanchor: *y*}` or longer) are redundant and the
last constraint encountered will be ignored to avoid
possible inconsistent constraints via `scaleratio`.
Note that setting axes simultaneously in both a
`scaleanchor` and a `matches` constraint is currently
forbidden. Setting `false` allows to remove a default
constraint (occasionally, you may need to prevent a
default `scaleanchor` constraint from being applied,
eg. when having an image trace `yaxis: {scaleanchor:
"x"}` is set automatically in order for pixels to be
rendered as squares, setting `yaxis: {scaleanchor:
false}` allows to remove the constraint).
scaleratio
If this axis is linked to another by `scaleanchor`,
this determines the pixel to unit scale ratio. For
example, if this value is 10, then every unit on this
axis spans 10 times the number of pixels as a unit on
the linked axis. Use this for example to create an
elevation profile where the vertical scale is
exaggerated a fixed amount with respect to the
horizontal.
separatethousands
If "true", even 4-digit integers are separated
shift
Moves the axis a given number of pixels from where it
would have been otherwise. Accepts both positive and
negative values, which will shift the axis either right
or left, respectively. If `autoshift` is set to true,
then this defaults to a padding of -3 if `side` is set
to "left". and defaults to +3 if `side` is set to
"right". Defaults to 0 if `autoshift` is set to false.
Only has an effect if `anchor` is set to "free".
showdividers
Determines whether or not a dividers are drawn between
the category levels of this axis. Only has an effect on
"multicategory" axes.
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of the
first tick is shown. If "last", only the exponent of
the last tick is shown. If "none", no exponents appear.
showgrid
Determines whether or not grid lines are drawn. If
True, the grid lines are drawn at every tick mark.
showline
Determines whether or not a line bounding this axis is
drawn.
showspikes
Determines whether or not spikes (aka droplines) are
drawn for this axis. Note: This only takes affect when
hovermode = closest
showticklabels
Determines whether or not the tick labels are drawn.
showtickprefix
If "all", all tick labels are displayed with a prefix.
If "first", only the first tick is displayed with a
prefix. If "last", only the last tick is displayed with
a suffix. If "none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
side
Determines whether a x (y) axis is positioned at the
"bottom" ("left") or "top" ("right") of the plotting
area.
spikecolor
Sets the spike color. If undefined, will use the series
color
spikedash
Sets the dash style of lines. Set to a dash type string
("solid", "dot", "dash", "longdash", "dashdot", or
"longdashdot") or a dash length list in px (eg
"5px,10px,2px,2px").
spikemode
Determines the drawing mode for the spike line If
"toaxis", the line is drawn from the data point to the
axis the series is plotted on. If "across", the line
is drawn across the entire plot area, and supercedes
"toaxis". If "marker", then a marker dot is drawn on
the axis the series is plotted on
spikesnap
Determines whether spikelines are stuck to the cursor
or to the closest datapoints.
spikethickness
Sets the width (in px) of the zero line.
tick0
Sets the placement of the first tick on this axis. Use
with `dtick`. If the axis `type` is "log", then you
must take the log of your starting tick (e.g. to set
the starting tick to 100, set the `tick0` to 2) except
when `dtick`=*L<f>* (see `dtick` for more info). If the
axis `type` is "date", it should be a date string, like
date data. If the axis `type` is "category", it should
be a number, using the scale where each category is
assigned a serial number from zero in the order it
appears.
tickangle
Sets the angle of the tick labels with respect to the
horizontal. For example, a `tickangle` of -90 draws the
tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the tick font.
tickformat
Sets the tick label formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. For numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.
And for dates see: https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two items to
d3's date formatter: "%h" for half of the year as a
decimal number as well as "%{n}f" for fractional
seconds with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f" would
display "09~15~23.46"
tickformatstops
A tuple of :class:`plotly.graph_objects.layout.yaxis.Ti
ckformatstop` instances or dicts with compatible
properties
tickformatstopdefaults
When used in a template (as
layout.template.layout.yaxis.tickformatstopdefaults),
sets the default property values to use for elements of
layout.yaxis.tickformatstops
ticklabelindex
Only for axes with `type` "date" or "linear". Instead
of drawing the major tick label, draw the label for the
minor tick that is n positions away from the major
tick. E.g. to always draw the label for the minor tick
before each major tick, choose `ticklabelindex` -1.
This is useful for date axes with `ticklabelmode`
"period" if you want to label the period that ends with
each major tick instead of the period that begins
there.
ticklabelindexsrc
Sets the source reference on Chart Studio Cloud for
`ticklabelindex`.
ticklabelmode
Determines where tick labels are drawn with respect to
their corresponding ticks and grid lines. Only has an
effect for axes of `type` "date" When set to "period",
tick labels are drawn in the middle of the period
between ticks.
ticklabeloverflow
Determines how we handle tick labels that would
overflow either the graph div or the domain of the
axis. The default value for inside tick labels is *hide
past domain*. Otherwise on "category" and
"multicategory" axes the default is "allow". In other
cases the default is *hide past div*.
ticklabelposition
Determines where tick labels are drawn with respect to
the axis Please note that top or bottom has no effect
on x axes or when `ticklabelmode` is set to "period".
Similarly left or right has no effect on y axes or when
`ticklabelmode` is set to "period". Has no effect on
"multicategory" axes or when `tickson` is set to
"boundaries". When used on axes linked by `matches` or
`scaleanchor`, no extra padding for inside labels would
be added by autorange, so that the scales could match.
ticklabelshift
Shifts the tick labels by the specified number of
pixels in parallel to the axis. Positive values move
the labels in the positive direction of the axis.
ticklabelstandoff
Sets the standoff distance (in px) between the axis
tick labels and their default position. A positive
`ticklabelstandoff` moves the labels farther away from
the plot area if `ticklabelposition` is "outside", and
deeper into the plot area if `ticklabelposition` is
"inside". A negative `ticklabelstandoff` works in the
opposite direction, moving outside ticks towards the
plot area and inside ticks towards the outside. If the
negative value is large enough, inside ticks can even
end up outside and vice versa.
ticklabelstep
Sets the spacing between tick labels as compared to the
spacing between ticks. A value of 1 (default) means
each tick gets a label. A value of 2 means shows every
2nd label. A larger value n means only every nth tick
is labeled. `tick0` determines which labels are shown.
Not implemented for axes with `type` "log" or
"multicategory", or when `tickmode` is "array".
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto", the number
of ticks is set via `nticks`. If "linear", the
placement of the ticks is determined by a starting
position `tick0` and a tick step `dtick` ("linear" is
the default value if `tick0` and `dtick` are provided).
If "array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`. ("array" is
the default value if `tickvals` is provided). If
"sync", the number of ticks will sync with the
overlayed axis set by `overlaying` property.
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If "", this
axis' ticks are not drawn. If "outside" ("inside"),
this axis' are drawn outside (inside) the axis lines.
tickson
Determines where ticks and grid lines are drawn with
respect to their corresponding tick labels. Only has an
effect for axes of `type` "category" or
"multicategory". When set to "boundaries", ticks and
grid lines are drawn half a category to the left/bottom
of labels.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position via
`tickvals`. Only has an effect if `tickmode` is set to
"array". Used with `tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud for
`ticktext`.
tickvals
Sets the values at which ticks on this axis appear.
Only has an effect if `tickmode` is set to "array".
Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud for
`tickvals`.
tickwidth
Sets the tick width (in px).
title
:class:`plotly.graph_objects.layout.yaxis.Title`
instance or dict with compatible properties
type
Sets the axis type. By default, plotly attempts to
determined the axis type by looking into the data of
the traces that referenced the axis in question.
uirevision
Controls persistence of user-driven changes in axis
`range`, `autorange`, and `title` if in `editable:
true` configuration. Defaults to `layout.uirevision`.
visible
A single toggle to hide the axis while preserving
interaction like dragging. Default is true when a
cheater plot is present on the axis, otherwise false
zeroline
Determines whether or not a line is drawn at along the
0 value of this axis. If True, the zero line is drawn
on top of the grid lines.
zerolinecolor
Sets the line color of the zero line.
zerolinewidth
Sets the width (in px) of the zero line.
Did you mean "tickfont"?
Bad property path:
titlefont
^^^^^^^^^
Caption: This bar chart contrasts CO₂ emissions per capita (left axis, orange bars) with healthy life expectancy (right axis, blue bars) for three example countries in 2019. The United States emits far more CO₂ per person (≈15 tons) than France (≈5 tons) or India (<2 tons). Yet, Americans have a shorter healthy life expectancy (~66 years) than the French (~72 years). Indians have a much lower healthy lifespan (~60 years) alongside very low emissions.
Insight: The comparison highlights that more emissions do not guarantee better health. The U.S. vs France gap is telling: Americans emit about 3× more CO₂ per capita, but enjoy roughly 6 fewer healthy years on average than the French. This could be due to pollution or other lifestyle and healthcare differences, perspective 1 would note that high emissions (often accompanied by pollution and greenhouse effects) might be undermining health in the U.S., which struggles with issues like air quality and chronic disease. Meanwhile, India shows the opposite extreme: very low emissions come with low healthy life expectancy, primarily due to poverty and limited healthcare. While India’s low emissions are not causing poor health (rather, they reflect less industrial development), perspective 1 advocates worry that as India’s emissions rise, environmental health burdens (e.g. smog in cities) could further challenge its progress. This case study suggests that beyond a certain point, increasing emissions is associated with diminishing health returns, France achieves higher health with lower emissions than the U.S.,aligning with the idea that cleaner development paths might support longer healthy lives.
Visualization 3. Air Quality and Health Trend – China as a Case (2000–2021)#
– Does rapid emission growth slow health progress?
Show code cell source
# ── Complete Cel: Plotly-plot met notebook_connected renderer ──
import pandas as pd
import plotly.graph_objects as go
import plotly.io as pio
# Zet renderer op notebook_connected voor inline weergave
pio.renderers.default = "notebook_connected"
# Data inladen & filteren
co2 = pd.read_csv('owid-co2-data.csv', usecols=['country','year','co2_per_capita'])
hale = pd.read_csv('C64284D_ALL_LATEST.csv')
# Harmoniseer & converteer jaartal
co2['year'] = co2['year'].astype(int)
hale = (
hale.loc[
(hale['IND_NAME']=='Healthy life expectancy (at birth)') &
(hale['DIM_SEX']=='TOTAL'),
['GEO_NAME_SHORT','DIM_TIME','AMOUNT_N']
]
.rename(columns={
'GEO_NAME_SHORT':'country',
'DIM_TIME':'year',
'AMOUNT_N':'healthy_life_expectancy'
})
)
hale['year'] = hale['year'].astype(int)
# Filter op China 2000–2021 en merge
co2_china = co2.query("country=='China' and 2000 <= year <= 2021")
hale_china = hale.query("country=='China' and 2000 <= year <= 2021")
china = pd.merge(co2_china, hale_china, on=['country','year']).sort_values('year')
# Bouw figuur
fig = go.Figure()
fig.add_trace(go.Scatter(
x=china['year'], y=china['co2_per_capita'],
name='CO₂ per Capita (tons)', mode='lines+markers',
marker=dict(color='orange'), line=dict(color='orange'),
yaxis='y1', hovertemplate='CO₂: %{y:.2f} ton<extra></extra>'
))
fig.add_trace(go.Scatter(
x=china['year'], y=china['healthy_life_expectancy'],
name='Healthy Life Expectancy (years)', mode='lines+markers',
marker=dict(color='blue'), line=dict(color='blue'),
yaxis='y2', hovertemplate='Gezonde levensverwachting: %{y:.1f} jaar<extra></extra>'
))
fig.update_layout(
title='China: Trend of CO₂ per Capita and Healthy Life Expectancy (2000–2021)',
xaxis=dict(title='Year'),
yaxis=dict(
title='CO₂ per Capita (tons)',
titlefont=dict(color='orange'),
tickfont=dict(color='orange')
),
yaxis2=dict(
title='Healthy Life Expectancy (years)',
titlefont=dict(color='blue'),
tickfont=dict(color='blue'),
overlaying='y', side='right'
),
legend=dict(orientation='h', yanchor='bottom', y=1.02, x=0.5, xanchor='center'),
margin=dict(t=80, b=50, l=60, r=60)
)
# Toon grafiek inline
fig.show()
Caption: This line chart shows China’s CO₂ emissions per capita (orange line, in tons) and healthy life expectancy at birth (blue line, in years) from 2000 to 2021. China’s CO₂ per person surged dramatically (more than tripling over two decades), while healthy life expectancy also rose steadily (from about 62 up to ~68 years).
Insight: China illustrates a nuanced story. Despite severe pollution challenges during its rapid industrialization, healthy life expectancy still improved significantly as the country became wealthier. There isn’t an obvious dip or slowdown in the upward health trend corresponding to rising emissions in fact, both lines climb upward. Advocates of perspective 1, however, argue that China’s health gains could have been even greater without the heavy air pollution that accompanied its coal-driven economic boom. In the 2010s, recognizing these issues, China enacted aggressive clean air policies. Noticeably, the curve of CO₂ per capita leveled off slightly toward 2019, and pollution levels in cities started dropping, which may help future health outcomes. This example underscores that while high emissions haven’t stopped health improvements outright, they likely impose hidden costs: respiratory illnesses, environmental stress, and fewer healthy years than might be possible in a cleaner environment. The full negative impact of emissions might be long-term through climate change and not fully captured in this 20-year window. Overall, the data for perspective 1 shows some hints (like the U.S. vs France comparison) that excessive emissions and pollution correlate with health drawbacks, but the relationship is complex and often outweighed by socioeconomic factors.
Perspective 2: Are Other Factors More Important than Emissions for Life Expectancy?#
Perspective 2 suggests that development and public health infrastructure drive healthy lifespan, not emissions per se. If this is true, we expect to see that as countries get wealthier (often accompanied by more emissions), their healthy life expectancy increases – indicating a positive or neutral relationship between emissions and health when development is accounted for. We also expect that countries with the longest healthy lifespans are those with strong healthcare and high living standards, rather than the lowest emitters. The following visuals explore these patterns.
Visualization 4. Historical CO₂ Emissions vs Healthy Life Expectancy#
– Do countries that industrialized (high historic emissions) have longer healthy lives?
Show code cell source
import plotly.express as px
# World map with CO2 per capita and Healthy Life Expectancy
fig = px.scatter_geo(
merged_df[merged_df['year'] == 2019],
locations='iso_code',
color='healthy_life_expectancy_at_birth',
size='co2_per_capita',
color_continuous_scale='Viridis',
hover_name='country',
title='Healthy Life Expectancy (color) and CO₂ Emissions per Capita (size) in 2019'
)
fig.show()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[30], line 4
1 import plotly.express as px
3 # World map with CO2 per capita and Healthy Life Expectancy
----> 4 fig = px.scatter_geo(
5 merged_df[merged_df['year'] == 2019],
6 locations='iso_code',
7 color='healthy_life_expectancy_at_birth',
8 size='co2_per_capita',
9 color_continuous_scale='Viridis',
10 hover_name='country',
11 title='Healthy Life Expectancy (color) and CO₂ Emissions per Capita (size) in 2019'
12 )
14 fig.show()
File c:\Users\haldo\anaconda3\Lib\site-packages\plotly\express\_chart_types.py:1148, in scatter_geo(data_frame, lat, lon, locations, locationmode, geojson, featureidkey, color, text, symbol, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, hover_name, hover_data, custom_data, size, animation_frame, animation_group, category_orders, labels, color_discrete_sequence, color_discrete_map, color_continuous_scale, range_color, color_continuous_midpoint, symbol_sequence, symbol_map, opacity, size_max, projection, scope, center, fitbounds, basemap_visible, title, template, width, height)
1101 def scatter_geo(
1102 data_frame=None,
1103 lat=None,
(...)
1142 height=None,
1143 ) -> go.Figure:
1144 """
1145 In a geographic scatter plot, each row of `data_frame` is represented
1146 by a symbol mark on a map.
1147 """
-> 1148 return make_figure(
1149 args=locals(),
1150 constructor=go.Scattergeo,
1151 trace_patch=dict(locationmode=locationmode),
1152 )
File c:\Users\haldo\anaconda3\Lib\site-packages\plotly\express\_core.py:2117, in make_figure(args, constructor, trace_patch, layout_patch)
2114 layout_patch = layout_patch or {}
2115 apply_default_cascade(args)
-> 2117 args = build_dataframe(args, constructor)
2118 if constructor in [go.Treemap, go.Sunburst, go.Icicle] and args["path"] is not None:
2119 args = process_dataframe_hierarchy(args)
File c:\Users\haldo\anaconda3\Lib\site-packages\plotly\express\_core.py:1513, in build_dataframe(args, constructor)
1510 args["color"] = None
1511 # now that things have been prepped, we do the systematic rewriting of `args`
-> 1513 df_output, wide_id_vars = process_args_into_dataframe(
1514 args, wide_mode, var_name, value_name
1515 )
1517 # now that `df_output` exists and `args` contains only references, we complete
1518 # the special-case and wide-mode handling by further rewriting args and/or mutating
1519 # df_output
1521 count_name = _escape_col_name(df_output, "count", [var_name, value_name])
File c:\Users\haldo\anaconda3\Lib\site-packages\plotly\express\_core.py:1234, in process_args_into_dataframe(args, wide_mode, var_name, value_name)
1232 if argument == "index":
1233 err_msg += "\n To use the index, pass it in directly as `df.index`."
-> 1234 raise ValueError(err_msg)
1235 elif length and len(df_input[argument]) != length:
1236 raise ValueError(
1237 "All arguments should have the same length. "
1238 "The length of column argument `df[%s]` is %d, whereas the "
(...)
1245 )
1246 )
ValueError: Value of 'locations' is not the name of a column in 'data_frame'. Expected one of ['country', 'year', 'co2_per_capita', 'healthy_life_expectancy_at_birth'] but received: iso_code
Caption: Each point is a country (2019), plotting total historical CO₂ emitted (on a log scale) against healthy life expectancy. A clear upward trend emerges: countries with a larger cumulative CO₂ footprint (toward the right) tend to have higher healthy life expectancy.
Insight: This chart reveals a strong positive association: nations that have emitted the most CO₂ over history, typically more developed economies, almost all enjoy high healthy life expectancies (70+ years). For instance, countries like Japan, Germany, the UK, or the U.S. (far right) have among the longest healthy lifespans. Conversely, countries with negligible historical emissions (far left) are generally those with shorter healthy lives (often under 60 years). This does not mean emitting CO₂ causes people to live longer; rather, it indicates that industrialization and development, which inevitably came with CO₂ emissions, enabled better health outcomes. In other words, wealth and infrastructure correlate with both high emissions and high life expectancy. This aligns with Perspective 2: healthy longevity is achieved through improved hospitals, nutrition, education, and living conditions which have historically been financed by the economic growth that also drove up emissions. The implication is that life expectancy can rise alongside emissions if development is occurring, and that cutting emissions need not reduce life expectancy as long as development and healthcare are maintained.
Visualization 5. Emissions vs Healthy Life Expectancy Over Time (Interactive)#
– How have emissions and health evolved together from 2000 to 2021?
Show code cell source
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.widgets import Slider
# 1) Data inladen en voorbereiden zoals je al deed
co2 = pd.read_csv('owid-co2-data.csv', usecols=['country','year','co2_per_capita'])
hale = pd.read_csv('C64284D_ALL_LATEST.csv')
hale = (
hale.loc[
(hale['IND_NAME']=='Healthy life expectancy (at birth)') &
(hale['DIM_SEX']=='TOTAL'),
['GEO_NAME_SHORT','DIM_TIME','AMOUNT_N']
]
.rename(columns={
'GEO_NAME_SHORT':'country',
'DIM_TIME':'year',
'AMOUNT_N':'healthy_life_expectancy'
})
)
co2['year'] = co2['year'].astype(int)
hale['year'] = hale['year'].astype(int)
df = pd.merge(co2, hale, on=['country','year'], how='inner')
df = df[(df.year >= 2000) & (df.year <= 2021)]
years = sorted(df['year'].unique())
# 2) Figuur en slider-as opzetten
fig, ax = plt.subplots(figsize=(8,6))
plt.subplots_adjust(bottom=0.15) # ruimte voor de slider
# Initiële scatter voor het eerste jaar
current_year = years[0]
sub = df[df.year == current_year]
scat = ax.scatter(sub['co2_per_capita'], sub['healthy_life_expectancy'],
s=50, edgecolor='k')
title = ax.set_title(f'Year: {current_year}')
ax.set_xlim(0, df['co2_per_capita'].max()*1.1)
ax.set_ylim(df['healthy_life_expectancy'].min()*0.9,
df['healthy_life_expectancy'].max()*1.05)
ax.set_xlabel('CO₂ per Capita (t/person)')
ax.set_ylabel('Healthy Life Expectancy (years)')
ax.grid(True, linestyle='--', alpha=0.5)
# Slider-as: [left, bottom, width, height] in fraction of fig
ax_slider = fig.add_axes([0.15, 0.05, 0.7, 0.03])
slider = Slider(
ax=ax_slider,
label='Year',
valmin=years[0],
valmax=years[-1],
valinit=current_year,
valstep=years,
color='lightblue'
)
# 3) Update-functie voor de slider
def update(val):
yr = int(slider.val)
sub = df[df.year == yr]
scat.set_offsets(np.c_[sub['co2_per_capita'], sub['healthy_life_expectancy']])
title.set_text(f'Year: {yr}')
fig.canvas.draw_idle()
slider.on_changed(update)
plt.show()
Caption: This interactive bubble chart (select the play button) shows countries moving from 2000 to 2021. Bubbles typically drift upwards and to the right over time, meaning both CO₂ emissions per capita and healthy life expectancy have increased in tandem for many countries. Insight: The animation reinforces that in the last two decades, life expectancy improvements often coincided with rising emissions. Developing countries (with lower starting health and emissions) move markedly up-right: for example, India and Bangladesh start near the bottom-left in 2000 (low emissions, ~50s HALE) and progress upward by 2019 (somewhat higher emissions, HALE in 60s). China’s bubble shoots to the right (big emission surge) and also climbs upward (HALE from low 60s to high 60s). Most high-income countries were already in the upper-right and tend to inch further up (health gains) even as their emissions per capita plateau or decline slightly. Notably, European countries have modest or falling CO₂ per capita but still improve health to around 70+ healthy years, showing that it’s possible to gain in health while curbing emissions. The overall picture supports Perspective 2: there is no general trade-off where increasing emissions universally lowers life expectancy if anything, countries have managed to raise healthy life expectancy substantially despite higher emissions. This suggests that improving medical care, sanitation, education, and incomes (which often come with industrial growth) has a more immediate and powerful effect on health than the hypothesized negative effects of CO₂. Of course, this does not mean CO₂-driven climate change has no future health impact; rather, up to 2021, socioeconomic progress appears to outweigh any direct life expectancy harms from emissions.
Visualization 6. Global Overview: Emissions and Health by Country (2019)#
– Who has high emissions, and who lives long and healthy?
# 1. Libraries importeren en renderer instellen
import pandas as pd
import plotly.express as px
import plotly.io as pio
# Kies de juiste renderer:
# - klassieke Notebook: 'notebook'
# - JupyterLab: 'jupyterlab'
pio.renderers.default = 'notebook'
# 2. WHO Healthy Life Expectancy inladen & filteren
who_df = pd.read_csv('C64284D_ALL_LATEST.csv')
who_df = who_df[
(who_df['IND_NAME'] == 'Healthy life expectancy (at birth)') &
(who_df['DIM_GEO_CODE_TYPE'] == 'COUNTRY')
]
who_df = who_df.rename(columns={
'GEO_NAME_SHORT': 'country',
'DIM_TIME': 'year',
'AMOUNT_N': 'healthy_life_expectancy_at_birth'
})[['country','year','healthy_life_expectancy_at_birth']]
who_df['year'] = who_df['year'].astype(int)
# 3. OWID CO₂ per capita inladen & selecteren
co2_df = pd.read_csv('owid-co2-data.csv')
co2_df = co2_df[['country','year','co2_per_capita']]
co2_df['year'] = co2_df['year'].astype(int)
# 4. Beide datasets mergen
merged_df = pd.merge(
co2_df,
who_df,
how='inner',
on=['country','year']
)
# 5. Direct inline tonen van de interactieve animatie
fig = px.scatter(
merged_df,
x='co2_per_capita',
y='healthy_life_expectancy_at_birth',
animation_frame='year',
animation_group='country',
hover_name='country',
range_x=[0,35],
range_y=[40,85],
labels={
'co2_per_capita': 'CO₂ per Capita (t/person)',
'healthy_life_expectancy_at_birth': 'Healthy Life Expectancy (years)'
},
title='Emissions vs Healthy Life Expectancy (2000–2021)'
)
fig.show()
Caption: In this world map, bubble size represents CO₂ emissions per capita and color represents healthy life expectancy (yellow-green = shorter healthy lives, blue-purple = longer healthy lives). We see large bubbles concentrated in North America, the Middle East, and parts of Asia/Oceania (indicating high per-person emissions), whereas small bubbles cover most of Africa and South Asia (minimal emissions). Crucially, many large bubbles are colored blue/purple – for example, the US, Canada, Australia, and Gulf states have high HALE (~65–75 years) despite high emissions. In contrast, the smallest bubbles (low emitters) in sub-Saharan Africa are often yellowish, showing low healthy life expectancy (50s).
Insight: This global view underscores that long healthy lives are achieved across a range of emission levels, and the worst health outcomes are mostly in low-emission, low-income countries. High emissions per capita are primarily a feature of wealthy nations and oil producers. These countries generally have the infrastructure for high life expectancy (though not always the very highest: e.g., the USA’s HALE is lower than some lower-emission European countries). Meanwhile, countries with the shortest healthy lifespans are poor and emit very little CO₂. This pattern supports the idea that economic and health system development are the dominant factors for healthy life expectancy, not CO₂ levels. If anything, the map suggests an injustice: those who have contributed least to emissions (and climate change) often have the lowest life expectancies. It also implies that reducing emissions in high-HALE countries (for sustainability) should be feasible without sacrificing their hard-won health outcomes, given their strong health systems. In summary, Perspective 2 finds that life expectancy is more strongly tied to wealth and public health investments than to carbon emissions. Policies focusing on improving healthcare, nutrition, and the environment together could continue to raise healthy life expectancy while also cutting unnecessary CO₂ emissions.